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Recent advances in reconstruction methods for inverse problems leverage powerful data-driven models, e.g., deep neural networks.
S. Kullback and R. A. Leibler, “On information and sufficiency,” Ann. Math. Statistics , vol. 22, pp. 79–86, 1951
1951
Earlier work this paper cites.
A. N. Tikhonov and V. Y. Arsenin, Solutions of Ill-posed Problems . V. H. Winston & Sons, Washington, D.C.: John Wiley & Sons, New York-Toronto, Ont.-London, 1977
1977
Earlier work this paper cites.
L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” 1992, vol. 60, no. 1-4, pp. 259–268
1992
Earlier work this paper cites.
H. W. Engl, M. Hanke, and A. Neubauer, Regularization of Inverse Problems . Kluwer Academic, Dordrecht, 1996
1996
Earlier work this paper cites.
J. Kaipio and E. Somersalo, Statistical and Computational Inverse Problems . Springer-Verlag, New York, 2005
2005
Earlier work this paper cites.
D. Gamerman and H. F. Lopes, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference . CRC Press, 2006
2006
Earlier work this paper cites.
C. M. Bishop, Pattern Recognition and Machine Learning . Springer, 2006
2006
Earlier work this paper cites.
K. Gregor and Y. LeCun, “Learning fast approximations of sparse coding,” in ICML , 2010, pp. 1–8
2010
Earlier work this paper cites.
A. Graves, “Practical variational inference for neural networks,” in NIPS , 2011, pp. 2348–2356
2011
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational Bayes,” arXiv:1312.6114 , 2013
2013
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. , vol. 15, no. 1, pp. 1929–1958, 2014
2014
Earlier work this paper cites.
K. Ito and B. Jin, Inverse Problems: Tikhonov Theory and Algorithms . World Scientific Publishing Co. Pte. Ltd., Hackensack, NJ, 2015
2015
Earlier work this paper cites.
C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intel. , vol. 38, no. 2, pp. 295–307, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
D. P. Kingma, T. Salimans, and M. Welling, “Variational dropout and the local reparameterization trick,” in NIPS , 2015, pp. 2575–2583
2015
Earlier work this paper cites.
J. Sun, H. Li, Z. Xu, and Y. Yan, “Deep ADMM-Net for compressive sensing MRI,” in NIPS , 2016, pp. 10–18
2016
Earlier work this paper cites.
Y. Gal, “Uncertainty in Deep Learning,” Ph.D. dissertation, University of Cambridge, 2016
2016
Cited alongside, same era.
Y. Gal and Z. Ghahramani, “Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,” in ICML , 2016, pp. 1050–1059
2016
Cited alongside, same era.
I. Osband, “Risk versus uncertainty in deep learning: Bayes, bootstrap and the dangers of dropout,” 2016
2016
Cited alongside, same era.
W. Van Aarle, W. J. Palenstijn, J. Cant, E. Janssens, F. Bleichrodt, A. Dabravolski, J. De Beenhouwer, K. J. Batenburg, and J. Sijbers, “Fast and flexible X-ray tomography using the ASTRA toolbox,” Optics Expr. , vol. 24, no. 22, pp. 25 129–25 147, 2016
2016
Cited alongside, same era.
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising,” IEEE Trans. Imag. Proc. , vol. 26, no. 7, pp. 3142–3155, 2017
J. Adler and O. Öktem, “Deep Bayesian inversion,” arXiv:1811.05910 , 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
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2017
Cited alongside, same era.
2017
Cited alongside, same era.
D. M. Blei, A. Kucukelbir, and J. D. McAuliffe, “Variational inference: A review for statisticians,” J. Amer. Stat Assoc. , vol. 112, no. 518, pp. 859–877, 2017
2017
Cited alongside, same era.
J. Adler, H. Kohr, and O. Oktem, “Operator discretization library (odl),” Software available from https://github.com/odlgroup/odl , 2017
2017
Cited alongside, same era.
K. Hammernik, T. Klatzer, E. Kobler, M. P. Recht, D. K. Sodickson, T. Pock, and F. Knoll, “Learning a variational network for reconstruction of accelerated MRI data,” Mag. Reson. Med. , vol. 79, no. 6, pp. 3055–3071, 2018
2018
Cited alongside, same era.
H. Gupta, K. H. Jin, H. Q. Nguyen, M. T. McCann, and M. Unser, “CNN-based projected gradient descent for consistent CT image reconstruction,” IEEE Trans. Med. Imag. , vol. 37, no. 6, pp. 1440–1453, 2018
2018
Cited alongside, same era.
J. Adler and O. Öktem, “Learned primal-dual reconstruction,” IEEE Trans. Med. Imag. , vol. 37, no. 6, pp. 1322–1332, 2018
2018
Cited alongside, same era.
B. Zhu, J. Z. Liu, S. F. Cauley, B. R. Rosen, and M. S. Rosen, “Image reconstruction by domain-transform manifold learning,” Nature , vol. 555, pp. 487–492, 2018
2018
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
A. Repetti, M. Pereyra, and Y. Wiaux, “Scalable Bayesian uncertainty quantification in imaging inverse problems via convex optimization,” SIAM J. Imaging Sci. , vol. 12, no. 1, pp. 87–118, 2019
2019
Later among the works it cites.
C. Zhang, J. Butepage, H. Kjellstrom, and S. Mandt, “Advances in variational inference,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 41, no. 8, pp. 2008–2026, 2019
2019
Later among the works it cites.
K. Osawa, S. Swaroop, M. E. E. Khan, A. Jain, R. Eschenhagen, R. E. Turner, and R. Yokota, “Practical deep learning with Bayesian principles,” in NIPS , 2019
2019
Later among the works it cites.
S. Rossi, P. Michiardi, and M. Filippone, “Good initializations of variational bayes for deep models,” in ICML , 2019, pp. 5487–5497
2019
Later among the works it cites.
2019
Later among the works it cites.
G. Ongie, A. Jalal, R. G. Baraniuk, C. A. Metzler, A. G. Dimakis, and R. Willett, “Deep learning techniques for inverse problems in imaging,” IEEE J. Sel. Areas Inf. Theory , pp. 39 – 56 in press, 2020
2020
Closest in time.
V. Antun, F. Renna, C. Poon, B. Adcock, and A. C. Hansen, “On instabilities of deep learning in image reconstruction and the potential costs of AI,” PNAS , 2020
2020
Closest in time.